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The generalized mathematical model uses uncertain parameters for content descriptors that are difficult to be precisely defined and measured, such as the level of intrusiveness and the change in performance over time.
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The model uses training data for classification by estimating uncertain quantities using the Bayes theorem.
Analysis of uncertainty is central in responsible decision making using uncertain data and models.
The goal of this paper is to estimate and quantify the uncertainty in damage modeled as a local reduction in stiffness using uncertain data.
The generative model used in this paper has no explicit representation of space but only the uncertain, vectorial position of a target.
However, irrespective of the marker model used, the extent to which these systems reproduce the actual joint angles under study remains uncertain.
Here, the same design model is used with uncertain parameters, and in addition, an uncertain controller gain, to reflect the uncertainty at vehicle launch.
The nonlinear system is represented locally by uncertain linear models using sector nonlinearities representation.
An extension of this method is proposed which enables to design PID controller guaranteeing robust stability and nominal performance for the uncertain plant modeled using unstructured uncertainty.
Both models use historical data based on uncertain parameters for previous periods to make decisions on future stages in a robust way.
This paper investigates the possibility of using the High Dimensional Model Representation (HDMR) approach when uncertain system parameters are modeled using fuzzy variables.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com